Knowledge graphs (KGs) have become vitally important in modern recommender
systems, effectively improving performance and interpretability. Fundamentally,
recommender systems aim to identify user interests based on historical
interactions and recommend suitable items. However, existing works overlook two
key challenges: (1) an interest corresponds to a potentially large set of
related items, and (2) the lack of explicit, fine-grained exploitation of KG
information and interest connectivity. This leads to an inability to reflect
distinctions between entities and interests when modeling them in a single way.
Additionally, the granularity of concepts in the knowledge graphs used for
recommendations tends to be coarse, failing to match the fine-grained nature of
user interests. This homogenization limits the precise exploitation of
knowledge graph data and interest connectivity. To address these limitations,
we introduce a novel embedding-based model called InBox. Specifically, various
knowledge graph entities and relations are embedded as points or boxes, while
user interests are modeled as boxes encompassing interaction history.
Representing interests as boxes enables containing collections of item points
related to that interest. We further propose that an interest comprises diverse
basic concepts, and box intersection naturally supports concept combination.
Across three training steps, InBox significantly outperforms state-of-the-art
methods like HAKG and KGIN on recommendation tasks. Further analysis provides
meaningful insights into the variable value of different KG data for
recommendations. In summary, InBox advances recommender systems through
box-based interest and concept modeling for sophisticated knowledge graph
exploitation.

通过盒子形式的兴趣和概念建模，InBox 推动推荐系统的发展，以实现对知识图谱的复杂利用。

使用知识图谱和兴趣盒嵌入进行推荐

InBox: Recommendation with Knowledge Graph using Interest Box Embedding

We propose a new problem called coordinated topic modeling that imitates
human behavior while describing a text corpus. It considers a set of
well-defined topics like the axes of a semantic space with a reference
representation. It then uses the axes to model a corpus for easily
understandable representation. This new task helps represent a corpus more
interpretably by reusing existing knowledge and benefits the corpora comparison
task. We design ECTM, an embedding-based coordinated topic model that
effectively uses the reference representation to capture the target
corpus-specific aspects while maintaining each topic's global semantics. In
ECTM, we introduce the topic- and document-level supervision with a
self-training mechanism to solve the problem. Finally, extensive experiments on
multiple domains show the superiority of our model over other baselines.

我们提出了一种名为 “协调主题建模” 的新问题，旨在通过复用现有知识来更加可解释地表示语料库，并设计了一种基于嵌入式的协调主题模型（ECTM），该模型通过引入主题和文档级别的监督和自训练机制来解决此问题，并在多个领域进行的广泛实验表明我们的模型优于其他基线。

协同主题建模

Coordinated Topic Modeling

The key to e-commerce search is how to best utilize the large yet noisy log
data. In this paper, we present our embedding-based model for grocery search at
Instacart. The system learns query and product representations with a two-tower
transformer-based encoder architecture. To tackle the cold-start problem, we
focus on content-based features. To train the model efficiently on noisy data,
we propose a self-adversarial learning method and a cascade training method.
AccOn an offline human evaluation dataset, we achieve 10% relative improvement
in RECALL@20, and for online A/B testing, we achieve 4.1% cart-adds per search
(CAPS) and 1.5% gross merchandise value (GMV) improvement. We describe how we
train and deploy the embedding based search model and give a detailed analysis
of the effectiveness of our method.

本文介绍了嵌入式模型在电商搜索中的应用，该模型由两个塔式变压器编码器构成，利用自适应对抗性学习和级联训练方法，以特别关注基于内容的特征并有效地在噪声大的数据上进行训练，检测出相对于原方案 10％的 RECALL@20 的改进并通过在线 A / B 测试达到了每次搜索中增加购物车数量（CAPS）和商品销售总值（GMV）方面 1.5％和 4.1％的提高。